Detecting short term, unusual events in a video is useful for many applications. For example, if the events are commercial segments of a broadcast video, then the user can rapidly skip over those segments to provide continuity of the underlying program. If the video is of a sporting event, for example football or golf, then highlights such as goals and puts can be detected rapidly. In a surveillance video, an intruders and traffic accidents are unusual events. Thus, unusual event detection is the foundation for video editing, summarization, indexing and browsing, and many other video processing applications.
Prior art event detection methods have mainly relied on identifying rules that measure attributes that are common to unusual or interesting events, such as black frames before the onset of a commercial, a large number of scene cuts, a high level of activity, perhaps combined with a louder or unusual audio track. Black frames can be detected by measuring the mean and variance of pixel intensities in frames. The high level of activity can be measured in terms of edge change ratio and motion vector length. Other rules consider the amount, size, style, and placement of text in a frame.
However, such rule based approaches assume a preconceived notion of the content, which is not supported by all videos, in general. For example, the rule for detecting commercials will fail for other short term events, such as scoring opportunities in a sport videos. The scene cut rule will fail for climatic scenes in action movies. The black frame rule is strictly dependent on a production style, which is not universally followed. Thus, rule based methods are unreliable in the general case.
Therefore, there is a need for a general data driven method that can detect short term, unusual events, independent of rules or content. Furthermore, it is desired that this method operates in the compressed domain.